Head of Engineering - Inbound Data

S&P Global•Washington, DC

About The Position

The Head of Engineering – Inbound Data will lead and scale an engineering organization of more than 100 data engineers, ML engineers, and platform developers. The role includes managing managers and Staff/Principal-level engineers and driving hiring, retention, and talent development. The team supports the division’s data strategy through data transformation, modernization of legacy systems, and close collaboration with product and business partners. The position provides strategic technical direction for enterprise-scale lakehouse architecture using the Databricks Data Intelligence Platform and AWS. It also leads the transition to production-grade autonomous AI systems, establishes engineering and operational standards, and oversees semantic modeling and RAG architecture. The role partners with Product, FinOps, and executive leadership to translate business goals into technical roadmaps, while cultivating psychological safety, continuous learning, and rapid experimentation.

Requirements

15+ years of engineering experience.
5+ years directly managing and scaling large engineering teams at Director or Head of Engineering level, including managing managers and guiding Staff/Principal-level individual contributors through organizational transformation.
Deep expertise in the Databricks Data Intelligence Platform, including Unity Catalog, Delta Lake internals, Delta Live Tables, Databricks Workflows, MLflow, and Mosaic AI.
Extensive experience architecting enterprise-scale data platforms on AWS, including Amazon S3, AWS Glue, Amazon EMR, and AWS Lake Formation; understanding of multi-region deployment strategies and IAM, VPC, and KMS security patterns.
Technical depth in Apache Iceberg internals, schema evolution, and cross-platform table strategies, plus hands-on experience deploying autonomous AI agents and production RAG pipelines.
Demonstrated ability to scale engineering organizations from 20+ to 100+ engineers and establish engineering standards, agile delivery practices, and operational SLAs for complex data and AI products.
Strong leadership and communication skills, including translating technical trade-offs for non-technical stakeholders and influencing executive decisions.

Nice To Haves

AWS and/or Databricks certifications.
Experience partnering with FinOps and cloud platform teams to optimize cloud consumption, performance, and platform ROI across multi-cloud data platform strategies.
Experience with semantic modeling, knowledge graphs, enterprise data governance, metadata management, and secure data sharing at enterprise scale.
Background in LLMOps, model evaluation, and prompt orchestration; familiarity with integrating Amazon SageMaker and Amazon Bedrock into enterprise AI platform strategies.
Experience with Terraform, CI/CD pipelines, deployment automation, and Databricks Asset Bundles for repeatable, auditable delivery across distributed cloud environments.

Responsibilities

Lead and scale an engineering organization of 100+ data engineers, ML engineers, and platform developers, including managing managers and Staff/Principal-level engineers.
Drive hiring, retention, and talent development strategies.
Set technical direction for enterprise-scale lakehouse architecture using Databricks and AWS, ensuring performant, cost-effective, interoperable data foundations.
Lead the transition to production-grade autonomous AI systems using Databricks AI tools and deliver measurable business value.
Establish engineering standards and operational excellence practices, including SLOs, error budgets, incident management, and infrastructure-as-code.
Oversee enterprise semantic modeling and RAG architecture, ensuring AI systems use governed data assets with appropriate access controls, lineage, and auditability.
Build a high-performance engineering culture centered on psychological safety, continuous learning, and rapid experimentation.
Partner with Product, FinOps, and executive leadership to translate business goals into actionable technical roadmaps.
Communicate technical trade-offs to non-technical stakeholders and influence executive decisions through data-driven recommendations and ROI analysis.